Evaluation of infrared thermography combined with behavioral biometrics for estrus detection in naturally cycling dairy cows
Bibliographic record
Abstract
Low estrus detection rates (>50%) are associated to extended calving intervals, low economic profit and reduced longevity in Holstein dairy cows. The objective of this study was to evaluate the accuracy of infrared thermography and behavioral biometrics combined as potential estrus alerts in naturally (not induced) cycling dairy cows housed in a tie-stall barn. Eighteen first lactation cows were subjected to transrectal ultrasonography to determine spontaneous ovulation. The dominant follicle (DF) disappearance was used retrospectively as an indirect indicator of ovulation, and to establish the estrus period (48-24 h prior the DF disappearance). Raw skin temperature (Raw IR) and residual skin temperature (Res IR) were recorded using an infrared camera at the Vulva area with the tail (Vtail), Vulva area without the tail (Vnotail), and Vulva's external lips (Vlips) at AM and PM milking from Day 14 until two days after ovulation was confirmed. Behavioral biometrics were recorded on the same schedule as infrared scan. Behavioral biometrics included large hip movements (L-hip), small hip movements (S-hip), large tail movements and small tail movements to compare behavioral changes between estrus and nonestrus periods. Significant increases in Raw IR skin temperature were observed two days prior to ovulation (Vtail; 35.93 ± 0.27 °C, Vnotail; 35.59 ± 0.27 °C, and Vlips; 35.35 ± 0.27 °C) compared to d -5 (Proestrus; Vtail; 35.29 ± 0.27 °C, Vnotail; 34.93 ± 0.31 °C, and Vlips; 34.68 ± 0.27 °C). No significant changes were found for behavioral parameters with the exception of S-hip movements, which increased at two days before ovulation (d -2; 11.13 ± 1.44 Events/5min) compared to d -5 (7.30 ± 1.02 Events/5min). To evaluate the accuracy of thermal and behavioral biometrics, receiver operating characteristic curve analysis was performed using Youden index (YJ), diagnostic odds ratio, positive likelihood ratio (LR+), Sensitivity, Specificity and Positive predicted value to score the estrus alerts. The greatest accuracy achieved using thermal parameters was for Res IR Vtail PM (YJ = 0.34) and L-hip PM (YJ = 0.27) for behavioral biometrics. Combining thermal and behavioral parameters did not improve the YJ index score but reduced the false-positive occurrence observed by increasing the diagnostic odds ratio (26.62), LR+ (12.47), Specificity (0.97) and positive predicted value (0.90) in a Res IR Vtail PM, S-hip AM, S-hip PM combination. The combination of thermal and behavioral parameters increased the accuracy of estrus detection compared to either thermal or behavioral biometrics, independently in naturally cycling cows during milking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".